How StrengthPals speeds up viral TikTok content production for a mobile fitness app
Strength Pals is a native mobile application for weightlifters, often described as “the Strava for weightlifters.” The platform focuses on tracking training, progress, and consistency for strength athletes.
To support user growth, Strength Pals operates a dedicated web application designed to systematize distribution. This web app transforms proven viral TikTok formats into repeatable, editable content that can be generated, refined, and published at scale.
The system combines automated content discovery, AI-driven reconstruction, and human-in-the-loop editing to maintain quality while increasing output volume.
Scaling TikTok content without turning editing into a core product
For Strength Pals, TikTok emerged as the most effective acquisition channel. High-performing content followed recognizable patterns, but reproducing those patterns manually was slow, inconsistent, and difficult to scale.
The challenge wasn’t generating ideas. It was operationalizing them.
The workflow required pulling viral TikTok content, extracting structure and text, rebuilding it into editable templates, generating variations, and preparing final assets for upload. Each step introduced edge cases, especially when OCR-driven text extraction produced imperfect positioning or overlapping elements.
At the same time, the editing layer was not the product. The mobile app and the distribution system around it were the priority. Building and maintaining a custom editor would have shifted focus away from automation, analytics, and publishing logic.
What was needed was an editor that could sit inside an automated pipeline, not dictate it. It had to support programmatic template creation, allow manual corrections when automation fell short, and remain flexible enough to evolve as the workflow expanded.
An embedded editor that fits inside an automated content workflow
Strength Pals embedded Polotno SDK as the editing layer inside its content generation system.
The editor is used at multiple stages. Viral TikTok content is pulled in, reconstructed into a template, and exposed through the editor. Variations are then generated programmatically, while still allowing edits before final output. When OCR or layout estimation produces inaccuracies, adjustments can be made directly in the editor and saved back into the template.
This approach keeps automation fast while preserving control.
Polotno’s Canva-like interaction model made it easy to work with templates visually without introducing friction. Editing controls are selectively exposed, keeping the interface focused on the specific needs of the workflow rather than a full design surface.
The editor also simplified a key technical requirement: producing a final composited asset that merges text and imagery correctly for TikTok delivery. Instead of building additional tooling for image composition and export, those capabilities were already available as part of the editor ecosystem.
Integration itself was straightforward. The complexity lived in the surrounding automation logic, not in the editor. This allowed development effort to stay concentrated on content sourcing, variation rules, publishing flows, and analytics.
A repeatable, scalable TikTok production system
With Polotno embedded, StrengthPals operates a content pipeline that balances automation with manual control.
Most content is generated automatically: templates are created from viral examples, variations are produced based on defined rules, and assets move through the pipeline with minimal intervention. When edge cases appear, such as misaligned text or OCR inaccuracies, corrections are made once and reused across future outputs.
The same editor layer supports every stage of the workflow, from initial template creation to final pre-publish adjustments. This consistency reduces friction and prevents tool sprawl as the system grows.
The result is a production-ready distribution engine that increases content throughput without turning editing into a separate product to build, maintain, and evolve independently. The editor functions as infrastructure: reliable, flexible, and aligned to the broader system rather than competing with it.